Performance monitoring system and method

ABSTRACT

A system and method for monitoring the performance of at least one machine operator, the system comprising at least one measuring device for measuring at least one machine parameter during operation of the machine by the operator, a server ( 8 ) for generating at least one performance indicator distribution from measurements of the at least one machine parameter and a performance indicator calculation module ( 18 ) for calculating at least one performance indicator from the at least one performance indicator distribution. Feedback may be provided to the operator by displaying the at least one performance indicator in substantially real-time to the operator on display module ( 6 ) onboard the machine.

The invention relates to a performance monitoring system and method. Inparticular, although not exclusively, the invention relates to a systemand method for monitoring the performance of equipment operators,particularly operators of draglines and shovels employed in mining andexcavation applications or the like.

BACKGROUND TO THE INVENTION

In many fields of manufacturing and industry, it is desirable ornecessary to monitor the performance of equipment operators in additionto the equipment itself. This may be for managerial purposes to ensurethat operators are complying with a minimum required standard ofperformance and to help Identify where improvements in performance maybe achieved. Monitoring performance may also be desired by an operatorto provide the operator with an indication of their own performance incomparison with other operators and to demonstrate their level ofcompetence to management.

One field in which performance monitoring is required is the operationof draglines and shovels and the like as used in large-scale mining andexcavation applications. For commercial purpose, it is important that anoperator is operating a piece of machinery to the best of the operator'sand the machine's capabilities.

There are however many factors that need to be measured and consideredto enable fair and useful comparisons to be made between differentoperators, between different machines, between present and previousperformances and between different operating conditions.

It is therefore desirable to provide a system and/or method capable ofachieving this objective. Furthermore, it is desirable thatperformance-monitoring information is promptly available to informmanagement and operators alike of current performance.

DISCLOSURE OF THE INVENTION

According to one aspect, although it need not be the only or indeed thebroadest aspect the invention resides in a method for monitoringperformance of at least one machine operator, the method including thesteps of:

measuring at least one machine parameter during operation of the machineby the operator;

generating at least one performance indicator distribution frommeasurements of the at least one machine parameter; and,

calculating at least one performance indicator from the at least oneperformance indicator distribution.

The method may further include the step of providing feedback to theoperator by displaying the at least one performance indicator insubstantially real-time to the operator. Alternatively, the at least oneperformance indicator may be displayed to the operator once the machinehas completed an operation cycle.

Suitably, the at least one machine parameter may be a dependent machineparameter. Alternatively, the at least one machine parameter may be thesole parameter represented by a particular performance indicator.

The method may further include the step of segmenting at least one ofthe dependent machine parameters into segments, the range of eachsegment constituting a segmentation resolution.

Suitably, the step of segmenting at least one of the dependent machineparameters includes specifying a magnitude of the range for each segmentof each dependent machine parameter requiring segmentation.

Suitably, at least one dependent machine parameter may not requiresegmentation.

Suitably, the step of generating the at least one performance indicatordistribution may comprise using a mixture of one or more distributionsto model the performance indicator distribution. The number of mixturesmay be set dynamically.

Suitably, the at least one performance indicator distribution may begenerated using an algorithm. The algorithm may be an LBG algorithm.Alternatively, the at least one performance indicator distribution maybe generated using a linear ranking model (LRM).

Suitably, two or more performance indicators may be combined to yield anoverall performance rating of the machine operator. One or more of theperformance indicators may be positively or negatively weighted withrespect to the other performance indicator(s).

According to another aspect, the invention resides in a system formonitoring performance of a machine operator, the system comprising:

at least one measuring device for measuring at least one machineparameter during operation of the machine by the operator;

a server for generating at least one performance indicator distributionfrom measurements of the at least one machine parameter; and,

a performance indicator calculation module for calculating at least oneperformance indicator from the at least one performance indicatordistribution.

Preferably, the server is remote from the machine.

Suitably, the server comprises storage means, communication means and aperformance indicator distribution calculation module.

Suitably, the performance indicator calculation mode is onboard themachine.

Preferably, the performance calculation module is coupled tocommunication means for transmitting and receiving data to and from thesender.

Preferably, the system further comprises at last one display device fordisplaying the at least one performance indicator in substantiallyreal-time to the operator. Alternatively, the at least one performanceindicator may be displayed to the operator once the machine hascompleted an operation cycle. The at least one display device may besituated in, on or about the machine and/or remote from the machine.

Suitably, the communication means comprises a transmitter and areceiver.

Further aspects of the invention become apparent from the followingdescription.

BRIEF DESCRIPTION OF THE DRAWINGS

To assist in understanding the invention and to enable a person skilledin the relevant art to put the invention into practical effect preferredembodiments will be described by way of example only and with referenceto the accompanying drawings, wherein:

FIG. 1 shows a distribution of data representing a production keyperformance indicator (KPI);

FIG. 2 is a schematic plan view of a machine showing segmentationresolution for the swing angle parameter;

FIG. 3 shows a distribution of Fill Production KPI data;

FIG. 4 shows dragline data for the parameters start fill reach versusstart fill height;

FIG. 5 shows calculation of a KPI for the right side of thedistribution;

FIG. 6 is a schematic representation of an integrated Mining Systems(IMS) system structure employed in the present invention;

FIG. 7 shows a display of KPIs showing current real-time performance anda comparison with performance for a previous cycle:

FIG. 8 shows a display of KPIs shoving current real-time performance;

FIG. 9 shows an alternative display of KPIs showing both currentreal-time performance and performance for a previous cycle;

FIG. 10 shows an Operator Performance Trend Report, and

FIG. 11 shows an Operator Ranking Report.

DETAILED DESCRIPTION OF THE INVENTION

The present invention monitors one or more parameters or variables of amachine to provide an accurate indication of how well an operator isperforming, for example, in comparison with other operators for the samemachine and/or in comparison with performances of the same operator.

Although the present invention will be described in the context ofmonitoring the performance of machine found on a mining site, it will beappreciated that the present invention is applicable to a wide varietyof machines found in various situations and performance monitoring isrequired.

A machine parameter may itself be referred to as a key performanceindicator (KPI). Alternatively, a KPI may be dependent on one or moremachine parameters. The KPIs may be represented and displayed as apercentage or a score, such as points scored out of 10, that describeshow well the operator is performing for a given parameter and/or KPI. Ahigh percentage value, such as >90% for example, shows that the operatoris performing extremely well. A mid-range value for a KPI, such as 50%for example, shows that the operator's performance is about average andless than this example percentage demonstrates that their performance isbelow average for that KPI.

Each KPI parameter is related to the performance of an operator for oneor more given machine parameters such as fill time, cycle time, digrate, and/or other parameter(s). KPIs are a measure of how the operatoris performing for the particular parameter related to that KPI comparedto the to operators. The performance of, or rating for, a particularoperator is calculated using. In part previous data record for themachine and provides an indication of whether or not the operator isimproving. The process for measuring the parameter and achieving theKPIs is described In detail hereinafter.

The parameter data is acquired using conventional measuring equipmentsuch as sensors, timing means and the like and the particular equipmentrequired to acquire the data would be familiar to a person of ordinaryskill in the relevant art.

Different comparisons the data are also possible. The current operatorof a machine can be compared to all the other operators of the samemachine or to the operator's previous performance(s). This shows howwell they perform against them and shows them whether they are improvingrespectively.

One Important consideration of the present invention is filtering thedata from all the machines that may be present in, for example, a minesite or other situation to enable fair and meaningful comparisons to bemade. Various factors that may affect KPI parameters are as follows:

Machine: Each machine possess different operating characteristics andtherefore the data from one machine will not reflect the performance ofoperating another machine.

Dig Mode: Different dig modes are possible with a single machine andthese may differ between different machines, which is significant. Inthe present invention operators can enter a particular dig modecorresponding to the mode of operation of the machine. The selected digmode must be correct otherwise the KPIs may be mis-represented andprovide misleading results.

Operator: Operators can compare their performance against their ownprevious performances to verify whether they are improving. Operator canalso compare their performances against those of other operators.

Location: Different locations in the mine will have different diggingconditions even though the digging made may be the same. This may berepresented by the specific gravity (s.g.) or by an Index that describesthe current digging difficulty, known as the dig index.

Bucket: Some KPIs will be affected by the type of bucket being used onthe dragline. For example, different size buckets, which are usuallypre-selected on the basis of the application, may produce different digrates. For comparison purposes, an operator should not be disadvantagedwhen using a smaller bucket.

Bucket Rigging: If this factor changes, but the bucket does not, the KPIresults may be affected.

Weather. The weather can change the digging conditions and thereforeaffect the performance attained by the operator.

Some of the above parameters are readily filtered from the data, such asmachine, dig mode, operator, bucket and possibly location. The more thedata is divided however, the more data need to be processed, stored andtransmitted from the server 8 to the onboard computer module 4 (shown inFIG. 6), to implement the KPIs. To reduce this volume of data thelocation parameter could optionally be omitted, since location data isgenerally reflected in the bucket type being used. Weather and bucketrigging are more difficult to filter. Therefore, the parameter filtersof machine, dig mode and bucket mode. These parameter filters may becombined with the operator parameter filter.

If the data of all operators are to be compared, the operator filter isomitted. When filtering by operator the number of operators multipliesthe amount of data for the mine comparison. For example, if there are1000 byte of KPI data to download to the module for the mine data andthere are 100 operators, then this equates to a total of 101,000 bytesof KPI data to download, which represents 100 data sets for 100operators plus one data set for the all operator comparison.

This large data problem is one of the problems addressed by the presentinvention, which enables the present invention to provide substantiallyreal-time monitoring of operators' performance.

The large data problem can be solved in a number of ways. One option isto only download KPI data for the operators that exist in the recordeddata in the database. Alternatively, only KPI data for operators thathave ever logged onto a particular machine, which is stored in anoperator profile, may be downloaded. For any new operator who logs on,the data is requested and downloaded. If the data does not exist in thedatabase, then the display can show that there is no KPI data for thatoperator. Another alternative is to just download the KPI data for theoperator that just logged on.

Even with the data filtering described above, a single value such asfill time, cannot be compared to other fill times unless one or moredependencies are introduced. Some KPIs, such as the Machine ReliabilityKPI, do not require a dependent parameter, but many do, such as theSwing Production KPI. A dependent parameter adds another level offiltering to the data that is specific to the parameter being ruled.

A simple example is the Swing Production KPI. The time taken to swing adragline, for example, is directly related to the angle through whichthe dragline swings (Swing Angle) and the vertical distance the buckettravels from the end of a fill to the top of a dump of the bucketcontents. These dependencies are included in the KPI calculation bysegmenting each of the dependent parameters into ranges. The range ofthe segment is called the segmentation resolution. The swing angle inthis example could be divided into 10- degree increments over, forexample, 380 degrees. If the vertical distance is ignored in thisexample, this would provide 36 data segments.

To calculate the KPI, the data recorded from that machine is sorted, forexample, by dig mode, for each of the segments. For the data associatedwith each segment, a KPI distribution is calculated. Therefore, for theSwing Production KPI example, the swing times for each angle segment areextracted and a distribution of times is calculated for each segment.Thus, 36 distributions would be calculated in total. The actual swingtimes and swing angles are measured onboard the machine usingconventional timing and angle measuring instrument that are familiar tothose skilled in the relevant art. The distribution associated with theswing angle segment being measured is then selected to calculate theKPI.

Introducing more dependent variables creates the problem of producingmore data segments, which in turn means more distributions and moredata. In the example above, if the vertical distance was included anddivided into, for example, 10 metre segments from 0 to +70 metres (7segments), there would be 252 (36×7) distributions to calculate anddownload to the machine just for the Swing Production KPI.

The volume of data can be reduced by carefully designing thesegmentation of the dependent parameters. One way is to includeextremities in the segmentation, which allows only segmentation of theareas that are common. In the above example, the swing angle could beresegmented such that one segment contains swing angles less than, forexample 30 degrees and another segment contains swing angles greaterthan, for example, 200 degrees whilst maintaining the 10-degree segmentsbetween 30 degrees and 200 degrees. This re-segmentation results in 19segments for the swing angle parameter compared with 36 in the previousexample.

The vertical height dependency could be reduced to 2 segments byidentifying the height at which the swing velocity is reduced (i.e. forhoist dependent swings). Less than this height is one segment and abovethis height is another. This reduces the total number of segments to 38(2×19) segments.

As described In the forgoing, a distribution for each segment of the KPIthat is dependent on some other parameter. Finding a distribution thatdescribes the KPI data is not trivial. Even though the sampled datalooks Gaussian in nature, the graphs are skewed and comprise some dataat the extremities.

FIG. 1 shows some data taken for the KPI representing production. Allthe offer KPIs show a similar distribution. FIG. 1 shows a positive skewIn the data and some data to the right of the graph. A simple Gaussianwould model most of this data quite adequately. However, it cannot bejudged how the data will skew or how the distribution will change oncethe KPI Information is available to the machine operator. It is likelythat the distribution will become more positively skewed and lessGaussian like.

One solution to this problem is to model the data with a multi-modal ormulti-variant Gaussian mixture in which a mixture of different Gaussiandistributions are used to model each KPI distribution. This has theadvantage that the number of mixtures can be changed depending on thedata. If the data is very Gaussian-like, then a single mixturecomprising a simple Gaussian distribution may be used. If the data isvery obscure, then a plurality of mixtures can be used to describe thedistribution.

The number of mixtures depends on the data that is being modeled and thenumber of mixtures may be set dynamically. With sufficient data, analgorithm could be employed to determine the maximum number of mixturesrequired to represent the KPI distribution. If there is only a smallamount of data, for example less than a selectable threshold of 10samples, then modeling may be carried out using a single mixture. If thealgorithm does not converge with the maximum number of mixtures, thehighest number of mixtures that cause the algorithm to converge can beused.

One algorithm that could be used to generate the distributions from thedata is a Linde-Buz-Gray (LBG) algorithm, which is known to personsskilled in the relevant art. The algorithm is an iterative algorithmthat splits data into a number of clusters. The algorithm is designedfor vectors, but in the present invention, single dimension vectors(single values) are used, thus simplifying the algorithm.

The detail of the LBG algorithm will now be described. X_(m)={x₁,x₂, . .. , x_(M)} is the training data set consisting of M data samples.C_(n)={c₁,c₂, . . . , c_(N)} are the centroid calculated for N clusters.c is the iteration conversion coefficient, which is usually fixed to asmall value greater than zero, such as 0.0.1.

The steps for generating the KPI distributions are as follows:

1. N=1 and given X, calculate initial centroid C₁ by calculating themean: $C_{1} = {\frac{1}{M}{\sum\limits_{m = 1}^{M}x_{m}}}$

2. Calculate the initial distortion of the data for the initialcentroid:$D_{avg}^{0} = {\frac{1}{M}{\sum\limits_{m = 1}^{M}{{x_{m} - c_{1}}}^{2}}}$

3. Set iteration index l=0.

4. Find the cluster p with the maximum distortion.

5. Increment the number of clusters: N=N+1

6. Split cluster p into 2:c _(P)=(1+δ)c _(P)c _(M)=(1−δ)c _(P)

7. For all 1≦m≦M in the data set X, record the nearest centroid c_(n*)^((i)) where n* is the index of the centroid.Q(x_(m))=c_(n*) ^((i))and the total number of values assigned to each centroid T_(n).

8. Calculate the new centroids: $\begin{matrix}{C_{m}^{({j + 1})} = \frac{\sum_{{Q{(x_{m})}} = c_{m}^{\lbrack j\rbrack}}x_{m}}{\sum_{{Q{(x_{m})}} = c_{m}^{\lbrack j\rbrack}}1}} \\{C_{m}^{({j + 1})} = \frac{\sum_{{Q{(x_{m})}} = c_{m}^{\lbrack j\rbrack}}x_{m}}{T_{m}}}\end{matrix}\quad{or}$

9. i=i+1.

10. Calculate the average of the minimum distortion between the datasample and its closest centroid:$D_{avg}^{1} = {\frac{1}{M}{\sum\limits_{m = 1}^{M}{{x_{m} - {Q\left( x_{m} \right)}}}^{2}}}$

11. If (D_(avg) ^((i<1))−D_(avg) ^((i)))/D_(avg) ^((i−1))>ε, then goback to step 7.

12. Save the temporary calculation centroids in a secure location.

13. If the number of desired clusters has not been reached, then go backto Step 4.

The algorithm starts by treating the whole of the data as one cluster.It then divides the cluster into two and iteratively assigns data toeach of the clusters until the centroids of the clusters do not moveappreciably. Once the iterations converge, the cluster with the greatestspread (accumulative distance between data and centroid) is split andthe iterative calculation are repeated. The algorithm continues untilthe required number of clusters has been reached. The result is datadivided into clusters with centroids. The data for each cluster is thenused to calculate a mean and standard deviation for that cluster, i.e. adistribution. The weight of each cluster is calculated as the number ofdata samples in the cluster compared to the total number of datasamples. This weight is known as the mixture coefficient.

In order to calculate the KPI from the distributions, the followingformula forp(x)=ΣC _(n) N(xμ,σ)a multi-variant Gaussian distribution is employed: where p(x) is theprobability, C_(n) is the mixture coefficient and N(x,μ,σ) isrepresented by the following formula:${N\left( {x,\mu,\sigma} \right)} = {\frac{1}{\sigma\sqrt{2x}}ɛ^{{- \frac{1}{2}}{(\frac{x - \mu}{\sigma})}^{2}}}$which is a standard Gaussian distribution with mean μ and standarddeviation σ.

Another solution to the problem of modeling the data to generate the KPIdistributions is to use a Linear Ranking Model (LRM). Instead ofmodeling the distribution of each of the segments for each KPI, the LRMmodels the distribution in such a way that only the minimum and maximumboundaries need to be calculated. All values between these limits arethen ranked according to their position between the minimum and maximum.This method has the advantage that is distribution independent.

One problem with the LRM is that is does not handle outlying data verywell. For example with reference to the Fill Production data shown inFIG. 3, there is an amount of data to the right of the graph (causedpossibly abnormal cycles). The minimum and maximum values respectivelyon the abscissa are 0.33 and 34 (unit=mass per unit time interval) forthis example. This means that the majority of the operators would obtaina low score and very few would obtain a high one since the majority ofFill Production values would occur in the lower half of the range.

A solution to this problem is to filter off the data. This can beachieved by removing data that is more than 3 standard deviations fromthe mean (keep 99% of the data for true Gaussian curve). The new minimumand maximum are −70 and 17.6. The negative minimum would be set to zeroand any values greater than the maximum are then deemed 100%.

Another consideration is that most of the scores obtained by theoperator will be around the average because we are modeling aGaussian-like distribution using a linear model. That is, as most of thedata is centered on the mean, the majority of the scores will be aroundthe mean. There is also the consideration that the scores arerepresented as a percentage, which no longer has a physical meaning.Instead, the operator will receive a score of 10.

The solution for the threshold problem is to calculable the thresholdsin the office. The mean sets the lower threshold so that if the operatorobtains a score below this then the operator is below average. For theupper threshold, the threshold for the top 10% of operators can befound. The data used to calculate these thresholds is all the date foreach KPI without segmentation. The threshold is then the average scoreof the thresholds over the KPIs. This means that we have a set thresholdfor all KPIs and one that does not vary from cycle to cycle.

The score for the KPI using the Linear Ranking Model is the ratiobetween the value and the difference of the minimum and maximum. Thisvalue is then multiplies by 10 to produce the KPI score. The followingequation shows the calculations required:${score} = {10 \times \frac{{value} - {minimum}}{{maximum} - {minimum}}}$

TABLE 1 below shows the advantages and disadvantages of the LRM and LBGmethods for generating the distributions. TABLE 1 Issue Gaussian ModelLinear Ranking Model Normal Models this well. Will have a small problemin that most Gaussian of the values concentrate around the curve mean soit is less likely for an operator to achieve above 80% and less than20%. This can be addressed by lowering the thresholds. Conceivably,these thresholds could be set dynamically in the office. Skewed Data Mayhave a problem if a Will handle this well. (After using lot of theoperators show KPIs for a an increase in while) performance. The worstof the best will actually be penalised by only receiving an averagescore. Low amount of Will only model the data Same problem as theGaussian Model data that it is given. but can be fixed by applyingmanual limits. Spurious data Handles this automatically. Filtering willneed to be applied to remove the outlying data. Taking the mean andremoving any data more than 3 standard deviations from the mean willhelp this. Maths Requires a clustering Simple minimum and maximum afteralgorithm to model the applying a simple Gaussian curve to data.filtered data. Upper and lower constraints can also be applied. OtherOnce implemented, the The way the limits are calculated can way the datais be changed with no changes to the represented cannot be on-boardsystem. changed easily.

The parameters represented by KPIs and their dependent parameters are:

1. Swing Production=Load Weight/Swing Time

-   -   Swing Angle    -   Hoist Dependent Swings

2. Fill Production=Load Weight/(Fill+Spot Times)

-   -   Start Fill Reach    -   Start Fill Height

3. Return Time

-   -   Swing Angle

4. Production Performance

-   -   This is a weighted sum of the 3 KPIs above.

5. Machine Reliability

Hence, there are 5 KPIs and 4 different dependent parameters. The HoistDependent Swings parameter does not require segmentation at all, as itis a Boolean. That leaves only 3 dependent parameters for whichsegmentation needs to be described.

However, it will be appreciated that the present invention is notlimited to the particular KPIs specified above, the number of KPIs, northe different dependent parameters. It is envisaged that otherparameters and KPIs and combinations thereof may be utilized in future,depending particularly on, for example, the particular application.

In accordance with the present invention, a segmentation resolution isset for each dependent parameter in the data structure, except for theHoist Dependent Swings parameter as previously explained. Thesegmentation resolution specifies the relevant variable(s), such asdistance, angle, and the like, for a single segment. For example, if thesegmentation resolution for Swing Angle were 15 degrees, then data wouldbe extracted for each 15-degree segment, an indicated In FIG. 2. Onlyfour segments are shown in FIG. 2. A weighted sum of the first 3 KPIsmay then be calculated to obtain an overall production performancerating.

Segmentation is performed from a single known point (such as the originin the case of the Start Fill Reach and Height). The data is thensegmented from this point based on the segmentation resolution asexplained above. Segments continue until the maximum or minimum limit isreached.

For example, FIG. 4 shows fill time data for different Fill and Heights.In the order of darkest to lightest shading of the data points, thepoints represent fill time, t, of t≦10s; 10<t≦20s; 20<t≦30s; and t≧30s.The segments would be divided such that they start at 0 cm and extendout to the 10,000 cm extremity for Fill Reach. For Fill Height; thesegments would extend up to the 1,000 cm extremity and down as far asthe −3,600 cm extremity.

The reason to perform the segmentation in this way is so that thedistributions represent a fixed set of conditions even after a period oftime. This way, data that was logged, for example, a month ago can befairly compared with current distributions.

Another setting for the KPIs related to the segmentation is thecalculation of a probability from the distribution. If a betterperformance is achieved by a lower KPI value, the right side of thedistribution needs to be calculated to obtain the KPI, as shown in FIG.8. The Return Time KPI is an example of such a KPI. The left side of thedistribution is calculated when a KPI value is required to be higher toachieve better performance. The Swing Production and Fill ProductionKPIs are examples of such a KPI.

FIG. 6 shown the structure of an integrated Mining Systems (IMS) system2. A Series 3 Computer Module 4 and associated Display Module 6 arelocated in each machine being monitored on site. An IMS server 8 mayalso be located on site, for example in the site office, or it may belocated at some other remote location providing communication within theTelemetry constraints is possible. The IMS server 8 comprises storagemeans in the form of a database 10, calculation means in the form of KPIdistribution calculation module 12, communication means in the form oftelemetry module 14 and application module 18 for the generation andediting of KPI reports.

The Database 10 also needs to store the KPI Distributions that aregenerated from the cycle data. A number of distributions are stored inthe Database 10. The first set of Distributions model the data for thatmachine for all operators. A set of Distributions will then exist foreach operator. The feedback onboard can then be compared to alloperators for that machine or to the currently logged on operator.

An overview of the Database Structure is described below. TABLE 2 KPIConfiguration Information Contents KPI Parameter ID Text description ofKPI Maximum number of Mixtures in a segment Left/Right distributionLength of moving average filter

The KPI Configuration information describes the global settings used Inthe system as shown in TABLE 2. The KPI Parameter ID identifies theparameter used in the calculation of the distributions and thecomparisons. The text description is used to display the KPI name on theReports/Form. The maximum number of mixtures is set here when using theLBG method. The maximum is likely to be 4, but this will probably varydepending on the KPI. The number of mixtures that are actually used canbe smaller than this number. The Left or Right distribution valuedetermines how to calculate the KPI onboard the machine. As discussedabove with reference to FIG. 5, it is a left distribution, then it meansthat a higher KPI variable is required to obtain better performance,e.g. Return Time. A right distribution means that a lower KPI isrequired to obtain better performance, e.g. Swing Production. A movingaverage can be optionally applied to the KPI result. TABLE 3 SegmentInformation Contents The ID of this segment KPI Parameter ID ID of themachine ID of the dig mode ID of the bucket ID of the operator

The Segment Information contains all the combinations of machines, digmodes, buckets, and operators in the mine for each KPI and associatedsegments as shown in TABLE 3. The KPI Distribution Calculation routineinserts all the entries into this table after it has determined thesegmentation of the data. The segment ID identifies the segment for thecurrent KPI, machine, dig mode, and the like. TABLE 4 SegmentationOffset Information Contents ID of the machine ID from Parameter LinkInformation Offset of the segment (om, degrees, etc.)

The Segmentation Offset Information contains the offset values fordependent parameters associated with a KPI as shown in Table 4. Theseneed to be configures for each machine for which KPI distributioncalculations will be performed. TABLE 5 Dependency Information ContentsThe ID of this segment The ID of the dependent parameter Lower limit ofdependent parameter Higher limit of dependent parameter

The Dependency Information contains the high and low limits for eachDistribution Calculation routine. TABLE 6 Distribution Information forthe LBG method Contents The ID of this segment Mixture weight of thedistribution Mean of the distribution Standard Deviation of thedistribution

The Distribution Information contains the distribution models for eachof the segments. The information stores here depends on the distributioncalculation method that is employed.

For the LBG method, TABLE 6 shows the information that is used. For eachsegment the mixture weight, mean and standard deviation are stored foreach mixture within the segment. TABLE 7 Distribution Information forthe LRM method. Contents The ID of this segment Maximum distributionvalue Minimum distribution value

For the LRM method, TABLE 7 shows the information that is used. For eachsegment the maximum and minimum distribution values are stored. TABLE 8Parameter Link Information Contents KPI Parameter ID The ID of aparameter Specifies whether or not the parameter is dependent

The Parameter Link Information shown in TABLE 8 is used to allowparameters to be associated with a KPI. Values for associated parametersthat are not dependent will be added to values for the KPI. Otherparameters are dependent parameters. TABLE 9 Parameter InformationContents The ID of a parameter Text description of the parameterThe Parameter Information shown in TABLE 9 is used to identify the KPIParameter ID with which the parameter is associated. This is used toidentify which KPI parameter and dependent parameters are used in themodeling.

The KPI Distribution Calculation routine is an NT service that isscheduled to run on a periodic basis.

The program collects the data, segments it and calculates thedistributions for each segment and stores the results in the Database10. While this program is running the system (mainly Telemetry module14) knows not to acquire any of the data from any of the KPI tables.This is because this program may take an order of hours to calculate allthe data. It may be necessary to set the priority of this task to low inthe system in case the processing time is significant.

The requirements for Telemetry are simple and would generally befamiliar to a person skilled in the art. The onboard computer module 4shown in FIG. 6 needs to request the KPI parameters that are currentlyin the database, but only if they have been changed. The onboard module4 will request the data for example, every 8 hours. If the KPIDistribution Calculation routine is running Telemetry needs to instructthe onboard module 4 to defer the request until later. It does this bysetting a KPI timestamp in the reply packet to zero.

The timestamp when the data was last changed is recorded in a table inthe database. The onboard module 4 will send an initial KPI requestpacket as described later herein. Telemetry replies with the basic KPIconfiguration data and the timestamp of when the service last ran. Ifthe service is running the timestamp is set to zero. The timestamp isalso sent with every packet during the download so that if the servicestarts while downloading, the onboard module 4 can detect that thetimestamp has gone to zero and it can abort the download.

The Telemetry Structure will now be described.

The onboard module 4 sends a KPI Configuration Request packet toTelemetry module 14 to request the KPI configuration. Telemetry module14 replies with a KPI Configuration packet, for which the contents areshown in Table 10. It places the timestamp in which the KPI DistributionCalculation Routine last ran into this packet. The onboard module thencompares this timestamp with the one it has to see if it needs to startdownloading the KPI segments. TABLE 10 KPI Configuration Packet ContentsThe timestamp of when the data was last updated. Number of KPIs in thedatabase The index of the KPI that we are replying to. KPI Parameter IDNumber of taps in the Moving average filter to apply to KPI output. Thegood to excellent threshold score (%) The poor to good threshold score(%)

A KPI Segment Request packet, as shown below in Table 11, requests thedata (distributions and the like) from Telemetry module 14. The reasonfor including the Dig Mode ID, bucket ID and the operator ID in thepacket is to enable prioritization of the download of the KPIdistributions if required.

The first packet contains a segment_index of 1 to request the firstsegment and subsequent packets contain the next segment that the systemwants. The requests stop when all the Segments for that machine havebeen downloaded. TABLE 11 KPI Segment Request packet Description KPIParameter ID Index to the segment for this KPI. The current dig modeentered on the machine. The current bucket on the machine. The currentlylogged on operator.

A KPI Segment packet shown in Table 12 below is the reply to the KPIsegment request packet. If there is no distribution for the segment,then the Distribution information contains nothing. TABLE 12 KPI Segmentpacket Contents The timestamp of when the data was last updated. TheTotal number of segments for this KPI (including ALL dig modes and ALLbuckets and ALL operators). KPI Parameter ID Dig mode ID of thisdistribution Bucket ID for this distribution Operator ID for thisdistribution The Segment ID Distribution Information The Productioncontribution of this segment Number of dependent parameters in thissegment First dependent parameter ID Lower limit of the dependentparameter Higher limit of the dependent parameter

The Series 3 Computer Mode 4 shown In FIG. 6 needs to download the KPIconfiguration and distribution information from the server 8, which isstored onboard in Flash memory. Once this information is downloaded,performance indicator calculation module 18 of onboard computer module 4is responsible for calculating the KPI scores after every cycle aspreviously described herein. If the LBG algorithm method described aboveis being used, a Gaussian lookup table may be used to calculate theGaussian curve instead of using the Gaussian distribution equationspecified above.

In order for the Series 3 Computer Module 4 to calculate the operator'sscore, it firstly selects the distribution by determining the segmentthat the current cycle matches for the particular KPI. Once thedistribution has been found, then the KPI score can be calculated. Ifthere exists no distribution to calculate a KPI, then the KPI score willbe 100% (or 10 if the LRM is being used).

The scores for all the KPIs are calculated for both the mine and currentoperator comparison. Therefore, there are 2 scores that need to becalculated for every KPI.

The KPI can be displayed on display module 8 as a real-time parameter inthe parameter list on a STATS screen. It may also be displayed as atrend so that the operator can see any performance improvements ordeteriorations. The trend may be configured by the operator to show thegraph for the last hour or the current shift or other suitable period.This is performed using the KPI trend configuration that is displayedonce the operator selects one of the trend graphs from a menu displayedon the STATS screen.

A third option is to display a KPI indicator that is again selected inthe trend configuration. Three different designs for the indicator areshown In FIGS. 7-9. The KPI indicator could appear white against a blackbackground to enhance visibility. FIG. 7 shows the current real-timeperformance. The arrows above each KPI indicate whether or not the scorehas improved from the last cycle. The extent to which the KPI hasimproved or deteriorated may also be shown. FIG. 8 shows an alternativemethod of displaying the real-time KPI scores for each of the KPIvariables including an overall performance rating, which may be theaverage of the KPI variable. FIG. 9 shows an alternative way ofdisplaying the scores for the previous cycle so that the operator canjudge any improvements or deteriorations from cycle to cycle. Thisversion could include more than just the last cycle.

The IMS Application module 16 preferably supports editing of at leastsome of the KPI Parameters. The following parameters need to beavailable to an administrator for editing: KPI text description: thesetting of the good and average thresholds for the KPI indicatorfrequency of running the KPI Distribution Calculation routine (KPIStatistical Generator); number of days of previous data to be used tocreate the models; display of the last time the KPI data was updated andthe like.

Reports, such as an Operator Performance Trend Report and an OperatorRanking Report, as shown in FIG. 10 and FIG. 11 respectively, may alsobe generated from the Report Manager in the IMS Application.

The Operator Performance Trend report shows the graphical trend of anoperator for each of the KPI variable. The options that should be madeto the person generating this report should include: Soft by machine,Sort by dig mode, Sort by bucket, Set Time period, Number of operatorsto show (top, specified number or all) and the KPIs to show.

The Operator Performance Trend report needs to calculate the KPI valuesover the selected time period based on the distributions contained inthe Database at the time. Therefore, the KPI scores need to becalculated again. The reason for this is that the scores that were shownto the operator onboard are no longer valid because the distributionswould have changed during that time and therefore cannot be compared toeach other. Because the Report Manager has to do these calculations, thereport may take a long time. Therefore the time period over which thetrends are calculated will have to be limited.

The operator Ranking report displays the ranking of operators for eachof the KPIs. That is, for a particular KPI or all KPIs, it displays theranking of all the operators. The time period needs to be selected and,as for the previous report, this time period will have to be limited asthe report may take a long time to run. This report needs to calculatewhat the previous report calculated, but needs to average the outputscreen.

The options that should be made to the person generating this reportshould include: Sort by machine, Sort by dig mode. Set Time period,Number of operators to show (top, specified number or all), The KPIs toshow.

An Average Production KPI may be provided that may be calculatedremotely and downloaded to the Series 3 computer module in the machine.This may be displayed on the performance graphs to show the operatortheir current performance relative to their average. This value can bedownloaded along with the operator ID lists.

Current practice used by all mines estimating operator performance onthe basis of Productivity appears to be wrong. Under differentconditions and production plans some of the operators could bedisadvantaged against others. For example, if an operator works in thesame conditions, but with different swing angles from another operator,productivity shown for the greater swing angle will be less than forsmaller swing angle, even though the first operator may in reality bemore efficient.

Taking into account that the number of effecting factors could include anumber of other parameters the applicant has identified that in order tobe able to compare product ranks of the same operator under differentconditions, some integrated value that could be used for rankingpurposes should be used.

In order to be able to calculate average rank for operators workingunder different conditions. Integration performance ranks achieved underdifferent conditions by different operators should be considered on theone hand and mine interests and production performance should beconsidered on another hand.

The suggested method of the present invention in this regard willinclude these 2 parameters as variables and will allow calculation ofaverage operator rank, which could be used as a universal rank among themine for different machines, conditions and production plans.

The formula for calculation of average operator rank is presented below:Av Op Rank=W ₁ *R ₁ +W ₂ *R ₂ +. . . +W ₁ *R ₁where: W₁—Weight coefficient for Parameter Subset i, which is calculatedon the basis of statistical information for the mine indicating theweight of I Parameter subset for the mine applicable to operator 1: andR_(i)—Rank of the operator i achieved for this Parameter Subset i.

For example, let it be assumed that during a reporting period a mineused only four different subsets of parameters. The weight of eachsubset could respectively be the following: 25%, 20%, 40% and 15%. Itoperator #1 worked only under subset #1 and #2 and achieved 90% forsubset #1 and 94% for subset #2, using the above formula the averagerank for the operator may be calculated:${{Av}\quad{Op}\quad{Rank}} = {{{\frac{25}{45} \times 90\quad\%} + {\frac{20}{45} \times 94\quad\%}} = {91.8\quad\%}}$For Operator #2, subset #3=92% and subset #4=90%. Hence:${{Av}\quad{Op}\quad{Rank}} = {{{\frac{40}{55} \times 92\quad\%} + {\frac{15}{55} \times 90\quad\%}} = {91.45\quad\%}}$

These Productivity ranks do not include Production figures and only rankoperators for different subsets of parameters. In reality, if, forexample, operator #1 was doing cycles with swings of say 10 and 20degrees and operator #2 swings of say 170 and 180 degrees, then the realproduction for operator #1 could be twice as much as for operator #2,but in fact the rank of operator #1 higher and accordingly he is better.

It is also conceivable that the average performance of an operator overthe last week or month could be shown. The average performance could becalculated remotely and the onboard module would download it to themachine for every operator. It would be treated just as a list downloadwhere one radio packet represents one graph. Only the minimum andmaximum values need to be sent and then each of the data points can bepercentage scaled.

Accurately determining one or more of the KPIs in accordance with thepresent invention addresses the difficulties of accurately measuringrelevant parameters and producing fair comparisons. The presentinvention can be used to improve awareness of how well the operators areperforming and provide an incentive to improve performance. It alsoprovides an indication to management about who is performing well andwhich operators are not performing up to standard.

Throughout the specification the aim has been to describe the inventionwithout limiting the invention to any one embodiment or specificcollection of features. Persons skilled in the relevant art may realizevariations from the specific embodiments that will nonetheless fallwithin the scope of the invention.

1. A method for monitoring performance of at least one machine operator,said method including the steps of: measuring at least one machineparameter during operation of the machine by the operator; generating atleast performance indicator distribution from measurements of the atleast one machine parameter; and, calculating at least one performanceindicator from the at least one performance indicator distribution. 2.The method of claim 1, further including the step of providing feedbackto the operator by displaying the at least one performance indicator insubstantially real-time to the operator.
 3. The method of claim 1,further including the step of providing feedback to the operator bydisplaying the at least one performance indicator to the operator oncethe machine has completed an operation cycle.
 4. The method of claim 1,wherein the at least one machine parameter is a dependent machineparameter.
 5. The method of claim 1, wherein the at least one machineparameter is the sole parameter represented by a performance indicator.6. The method of claim 4, further including the step of segmenting atleast one of the dependent machine parameters into segments, the rangeof each segment constituting a segmentation resolution.
 7. The method ofclaim 6, wherein the step of segmenting at least one of the dependentmachine parameters includes specifying a magnitude of the range for eachsegment of each dependent machine parameter requiring segmentation. 8.The method of claim 4, wherein at least one dependent machine parameterdoes not require segmentation.
 9. The method of claim 1, wherein thestep of generating the at least one performance indicator distributionincludes: using a mixture of one or more distributions to model theindicator distribution.
 10. The method of claim 9, wherein the number ofmixtures is set dynamically.
 11. The method of claim 1, wherein the atleast one performance indicator distribution is generated using analgorithm.
 12. The method of claim 11, wherein the algorithm is aLinde-Buzo-Gray (LBG) algorithm.
 13. The method of claim 1, wherein theat least one performance indicator distribution is generated using alinear ranking model (LRM).
 14. The method of claim 1, wherein two ormore performance indicators are combined to yield an overall performancerating of the machine operator.
 15. The method of claim 14, wherein oneor more of the performance indicators are positively or negativelyweighted with respect to the other performance indicator(s).
 16. Asystem for monitoring performance of at least one machine operator, saidsystem comprising: at least one measuring device for measuring at leastone machine parameter during operation of the machine by the operator; aserver for generating at least one performance indicator distributionfrom measurements of the at least one machine parameter; and, aperformance indicator calculation module for calculating at least oneperformance indicator from the at least one performance indicatordistribution.
 17. The system of claim 16, wherein the server is remotefrom the machine.
 18. The system of claim 16, wherein the servercomprises: storage means; communication means; and a performanceindicator distribution calculation module.
 19. The system of claim 16,wherein the performance indicator calculation module is onboard themachine.
 20. The system of claim 16, wherein the performance indicatorcalculation module is coupled to communication means for transmittingand receiving data to and from the server.
 21. The system of claim 16,comprising at least one display device.
 22. The system of claim 21,wherein the at least one display device displays the at least oneperformance indicator in substantially real-time to the operator. 23.The system of claim 21, wherein the at least one display device displaysthe at least one performance indicator to the operator once the machinehas completed an operation cycle.
 24. The system of claim 21, whereinthe at least one display device is onboard the machine.
 25. The systemof claim 21, wherein the at least one display device is remote from themachine.